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Data visualisation

library(tidyverse)

## Loading tidyverse: ggplot2
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr

## Conflicts with tidy packages ----------------------------------------------

## filter(): dplyr, stats
## lag():    dplyr, stats

tidyverse_packages()  # which packages are in tidyverse

##  [1] "broom"     "dplyr"     "forcats"   "ggplot2"   "haven"    
##  [6] "httr"      "hms"       "jsonlite"  "lubridate" "magrittr" 
## [11] "modelr"    "purrr"     "readr"     "readxl"    "stringr"  
## [16] "tibble"    "rvest"     "tidyr"     "xml2"      "tidyverse"

knitr::opts_chunk$set(cache=TRUE) # renders only changes => faster

Where can I find useful packages?

CRAN “task views” https://cran.r-project.org
r-bloggers search http://www.r-bloggers.com

Where can I find how to use packages

Reference manual on CRAN
Vignettes
?
Demos

# List vignettes from all *attached* packages
vignette(all = FALSE)
# List vignettes from all *installed* packages (can take a long time!):
vignette(all = TRUE)
# find vignettes of "ggplot2"
vignette(package = "ggplot2")
# view vignette "ggplot2-specs"  
vignette("ggplot2-specs")

## starting httpd help server ...

##  done

now look for more information on ggplot

?ggplot2
demo()          # find demos for attached packages
demo(graphics)  # A show of some of R's graphics capabilities, run in console

## 
## 
##  demo(graphics)
##  ---- ~~~~~~~~
## 
## > #  Copyright (C) 1997-2009 The R Core Team
## > 
## > require(datasets)
## 
## > require(grDevices); require(graphics)
## 
## > ## Here is some code which illustrates some of the differences between
## > ## R and S graphics capabilities.  Note that colors are generally specified
## > ## by a character string name (taken from the X11 rgb.txt file) and that line
## > ## textures are given similarly.  The parameter "bg" sets the background
## > ## parameter for the plot and there is also an "fg" parameter which sets
## > ## the foreground color.
## > 
## > 
## > x <- stats::rnorm(50)
## 
## > opar <- par(bg = "white")
## 
## > plot(x, ann = FALSE, type = "n")

## 
## > abline(h = 0, col = gray(.90))
## 
## > lines(x, col = "green4", lty = "dotted")
## 
## > points(x, bg = "limegreen", pch = 21)
## 
## > title(main = "Simple Use of Color In a Plot",
## +       xlab = "Just a Whisper of a Label",
## +       col.main = "blue", col.lab = gray(.8),
## +       cex.main = 1.2, cex.lab = 1.0, font.main = 4, font.lab = 3)
## 
## > ## A little color wheel.    This code just plots equally spaced hues in
## > ## a pie chart.    If you have a cheap SVGA monitor (like me) you will
## > ## probably find that numerically equispaced does not mean visually
## > ## equispaced.  On my display at home, these colors tend to cluster at
## > ## the RGB primaries.  On the other hand on the SGI Indy at work the
## > ## effect is near perfect.
## > 
## > par(bg = "gray")
## 
## > pie(rep(1,24), col = rainbow(24), radius = 0.9)

## 
## > title(main = "A Sample Color Wheel", cex.main = 1.4, font.main = 3)
## 
## > title(xlab = "(Use this as a test of monitor linearity)",
## +       cex.lab = 0.8, font.lab = 3)
## 
## > ## We have already confessed to having these.  This is just showing off X11
## > ## color names (and the example (from the postscript manual) is pretty "cute".
## > 
## > pie.sales <- c(0.12, 0.3, 0.26, 0.16, 0.04, 0.12)
## 
## > names(pie.sales) <- c("Blueberry", "Cherry",
## +              "Apple", "Boston Cream", "Other", "Vanilla Cream")
## 
## > pie(pie.sales,
## +     col = c("purple","violetred1","green3","cornsilk","cyan","white"))

## 
## > title(main = "January Pie Sales", cex.main = 1.8, font.main = 1)
## 
## > title(xlab = "(Don't try this at home kids)", cex.lab = 0.8, font.lab = 3)
## 
## > ## Boxplots:  I couldn't resist the capability for filling the "box".
## > ## The use of color seems like a useful addition, it focuses attention
## > ## on the central bulk of the data.
## > 
## > par(bg="cornsilk")
## 
## > n <- 10
## 
## > g <- gl(n, 100, n*100)
## 
## > x <- rnorm(n*100) + sqrt(as.numeric(g))
## 
## > boxplot(split(x,g), col="lavender", notch=TRUE)

## 
## > title(main="Notched Boxplots", xlab="Group", font.main=4, font.lab=1)
## 
## > ## An example showing how to fill between curves.
## > 
## > par(bg="white")
## 
## > n <- 100
## 
## > x <- c(0,cumsum(rnorm(n)))
## 
## > y <- c(0,cumsum(rnorm(n)))
## 
## > xx <- c(0:n, n:0)
## 
## > yy <- c(x, rev(y))
## 
## > plot(xx, yy, type="n", xlab="Time", ylab="Distance")

## 
## > polygon(xx, yy, col="gray")
## 
## > title("Distance Between Brownian Motions")
## 
## > ## Colored plot margins, axis labels and titles.    You do need to be
## > ## careful with these kinds of effects.    It's easy to go completely
## > ## over the top and you can end up with your lunch all over the keyboard.
## > ## On the other hand, my market research clients love it.
## > 
## > x <- c(0.00, 0.40, 0.86, 0.85, 0.69, 0.48, 0.54, 1.09, 1.11, 1.73, 2.05, 2.02)
## 
## > par(bg="lightgray")
## 
## > plot(x, type="n", axes=FALSE, ann=FALSE)

## 
## > usr <- par("usr")
## 
## > rect(usr[1], usr[3], usr[2], usr[4], col="cornsilk", border="black")
## 
## > lines(x, col="blue")
## 
## > points(x, pch=21, bg="lightcyan", cex=1.25)
## 
## > axis(2, col.axis="blue", las=1)
## 
## > axis(1, at=1:12, lab=month.abb, col.axis="blue")
## 
## > box()
## 
## > title(main= "The Level of Interest in R", font.main=4, col.main="red")
## 
## > title(xlab= "1996", col.lab="red")
## 
## > ## A filled histogram, showing how to change the font used for the
## > ## main title without changing the other annotation.
## > 
## > par(bg="cornsilk")
## 
## > x <- rnorm(1000)
## 
## > hist(x, xlim=range(-4, 4, x), col="lavender", main="")

## 
## > title(main="1000 Normal Random Variates", font.main=3)
## 
## > ## A scatterplot matrix
## > ## The good old Iris data (yet again)
## > 
## > pairs(iris[1:4], main="Edgar Anderson's Iris Data", font.main=4, pch=19)

## 
## > pairs(iris[1:4], main="Edgar Anderson's Iris Data", pch=21,
## +       bg = c("red", "green3", "blue")[unclass(iris$Species)])

## 
## > ## Contour plotting
## > ## This produces a topographic map of one of Auckland's many volcanic "peaks".
## > 
## > x <- 10*1:nrow(volcano)
## 
## > y <- 10*1:ncol(volcano)
## 
## > lev <- pretty(range(volcano), 10)
## 
## > par(bg = "lightcyan")
## 
## > pin <- par("pin")
## 
## > xdelta <- diff(range(x))
## 
## > ydelta <- diff(range(y))
## 
## > xscale <- pin[1]/xdelta
## 
## > yscale <- pin[2]/ydelta
## 
## > scale <- min(xscale, yscale)
## 
## > xadd <- 0.5*(pin[1]/scale - xdelta)
## 
## > yadd <- 0.5*(pin[2]/scale - ydelta)
## 
## > plot(numeric(0), numeric(0),
## +      xlim = range(x)+c(-1,1)*xadd, ylim = range(y)+c(-1,1)*yadd,
## +      type = "n", ann = FALSE)

## 
## > usr <- par("usr")
## 
## > rect(usr[1], usr[3], usr[2], usr[4], col="green3")
## 
## > contour(x, y, volcano, levels = lev, col="yellow", lty="solid", add=TRUE)
## 
## > box()
## 
## > title("A Topographic Map of Maunga Whau", font= 4)
## 
## > title(xlab = "Meters North", ylab = "Meters West", font= 3)
## 
## > mtext("10 Meter Contour Spacing", side=3, line=0.35, outer=FALSE,
## +       at = mean(par("usr")[1:2]), cex=0.7, font=3)
## 
## > ## Conditioning plots
## > 
## > par(bg="cornsilk")
## 
## > coplot(lat ~ long | depth, data = quakes, pch = 21, bg = "green3")

## 
## > par(opar)

Analyse data

note that the pipe can be run in parts (short cut Ctrl+Shift+M, CMD+SHIFT+M )

mpg  %>% select(displ, cty, hwy, year)  %>% plot()
# compare this to the "nested" version 
plot(select(mpg,displ,cty,hwy,year))

Creating a ggplot

“ggplot” is part of the “tidyverse” and a widely used package to work with graphics note for ggplot there is “+” to combine commands, in contrast to “% > %” which is the pipe operator for commands outside ggplot

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

Create a ggplot with color = class

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class))

Create a ggplot with size = cty

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, size = cty))

Create a ggplot with alpha = class

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, alpha = class))

Create a ggplot with shape = class

note there are only 6 different shapes, therefore “suv” has no shape and is not displayed

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, shape = class))

## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have 7.
## Consider specifying shapes manually if you must have them.

## Warning: Removed 62 rows containing missing values (geom_point).

Create plot where property of geom is set manually

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy), color = "blue")

Recap

Where would you check for packages?
Where would you look on how to use packages?
When would you use size as function of a variable in a plot?

Facets

If there is a variable value which separates data it can be used to create multiple plots rather than multiple lines in one plot.

facet_grid

facet_grid forms a matrix of panels defined by row and column facetting variables.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_grid(drv ~ cyl)

Now get your hands dirty on size, color, shape, alpha

Analyse available data set in ggplot2! The data sets are listed and explained @ http://docs.ggplot2.org

Use - size - color - alpha - shape

to emphasise you message

ggplot(midwest, aes(x= area, y = poptotal, color = percchildbelowpovert)) + geom_point() + facet_wrap(~ state, nrow = 3) + scale_y_log10() + scale_x_log10()

Geometic objects

different ways to present the same data

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

ggplot(data = mpg) + 
  geom_smooth(mapping = aes(x = displ, y = hwy))

## `geom_smooth()` using method = 'loess'

geom_smooth with more than one line

draw a different line, with a different linetype, for each unique value of the variable that you map to linetype

ggplot(data = mpg) + 
  geom_smooth(mapping = aes(x = displ, y = hwy, linetype = drv, color = drv))

## `geom_smooth()` using method = 'loess'

avoid the legend

ggplot(data = mpg) +
  geom_smooth(mapping = aes(x = displ, y = hwy, group = drv))

## `geom_smooth()` using method = 'loess'

display several geoms in same plot

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) +
  geom_smooth(mapping = aes(x = displ, y = hwy))

## `geom_smooth()` using method = 'loess'

don’t repeat code

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point(mapping = aes(color = class)) + 
  geom_smooth()

## `geom_smooth()` using method = 'loess'

use only subset of data for geom

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
geom_point(mapping = aes(color = class)) + 
  geom_smooth(data = filter(mpg, class == "subcompact"), se = FALSE)

## `geom_smooth()` using method = 'loess'

bar plot for discrete x-data

ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut))

lost in all the options?

CHEAT SHEETS are at your fingertips under HELP menu of RStudio IDE or https://www.rstudio.com/resources/cheatsheets/
stackoverflow is a vivid community http://stackoverflow.com
RDocumentation searches CRAN, BioConductor and Github packages https://www.rdocumentation.org

Statistical transformations

box plot for discrete x- and continuous y-data

ggplot(data = diamonds) + 
  geom_boxplot(mapping = aes(x = cut, y = price))

Violin plot for discrete x- and continuous y-data

gives good impression of distribution

ggplot(data = diamonds) + 
  geom_violin(mapping = aes(x = cut, y = price, color = cut))

Histogram

A histogram is a graphical representation of the distribution of numerical data.

https://de.wikipedia.org/wiki/Histogramm

ggplot(diamonds, aes(carat)) +
  geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# set binwidth
ggplot(diamonds, aes(carat)) +
  geom_histogram(binwidth = 0.01)

# set number of bins
ggplot(diamonds, aes(carat)) +
  geom_histogram(bins = 200)

use geom_freqpoly for easier comparison

# Rather than stacking histograms, it's easier to compare frequency
# polygons
ggplot(diamonds, aes(price, fill = cut)) +
  geom_histogram(binwidth = 500)

ggplot(diamonds, aes(price, colour = cut)) +
  geom_freqpoly(binwidth = 500)

work with densities, means each curve has area of one

# To make it easier to compare distributions with very different counts,
# put density on the y axis instead of the default count
ggplot(diamonds, aes(price, ..density.., colour = cut)) +
  geom_freqpoly(binwidth = 500)

Empirical Cumulative Distribution Function (ECDF)

The empirical distribution function estimates the cumulative distribution function underlying of the points in the sample and converges with probability 1

https://de.wikipedia.org/wiki/Empirische_Verteilungsfunktion

df <- data.frame(x = rnorm(10000))
ggplot(df, aes(x)) +
  geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(df, aes(x)) + stat_ecdf(geom = "step")

p  <- ggplot(df, aes(x)) + stat_ecdf()
pg <- ggplot_build(p)$data[[1]]
ggplot(pg, aes(x = x, y = 1-y )) + geom_step() + scale_y_log10()

## Warning: Transformation introduced infinite values in continuous y-axis

Find correlations

In statistics relationship between two variables.

library(corrplot)
cor_iris <- cor(iris %>% select(-Species))
corrplot.mixed(cor_iris)

further details on the corrplot package can be found in the vignette

vignette("corrplot-intro")

Maximal Information Coefficient (MIC)

In statistics, the maximal information coefficient (MIC) is a measure of the strength of the linear or non-linear association between two variables X and Y.

library(minerva)
compare_mic_r = function(x, y){
cat( "MIC:", mine(x,y)$MIC, ";", "correlation: ", cor(x,y), "\n")  
}
x <- runif(n=1000, min=0, max=1)
y2 <- 4*(x-0.5)^2; plot(sort(x),y2[order(x)],type="l"); compare_mic_r(x,y2)

## MIC: 1 ; correlation:  0.02845333

y3 <- sin(6*pi*x*(1+x)); plot(sort(x),y3[order(x)],type="l"); compare_mic_r(x,y3)

## MIC: 1 ; correlation:  -0.1290837

t <- seq(from=0,to=2*pi,length.out=1000)
x4 <- cos(t); y4 <- sin(t); plot(x4, y4, type="l",asp=1); compare_mic_r(x4,y4)

## MIC: 0.6829015 ; correlation:  5.798018e-18

Recap

Which geom seems useful for you?
Any idea where could use facet plots for one of your tasks?
What do you think about correlation and maximum information coefficient?

Data wrangling

library(nycflights13)
flights

## # A tibble: 336,776 × 19
##     year month   day dep_time sched_dep_time dep_delay arr_time
##    <int> <int> <int>    <int>          <int>     <dbl>    <int>
## 1   2013     1     1      517            515         2      830
## 2   2013     1     1      533            529         4      850
## 3   2013     1     1      542            540         2      923
## 4   2013     1     1      544            545        -1     1004
## 5   2013     1     1      554            600        -6      812
## 6   2013     1     1      554            558        -4      740
## 7   2013     1     1      555            600        -5      913
## 8   2013     1     1      557            600        -3      709
## 9   2013     1     1      557            600        -3      838
## 10  2013     1     1      558            600        -2      753
## # ... with 336,766 more rows, and 12 more variables: sched_arr_time <int>,
## #   arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
## #   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## #   minute <dbl>, time_hour <dttm>

filter rows

filter all rows where month == 1 and day == 1, multiple filter conditions are separated by “,” and are treated as logical “AND”

filter(flights, month == 1, day == 1)

## # A tibble: 842 × 19
##     year month   day dep_time sched_dep_time dep_delay arr_time
##    <int> <int> <int>    <int>          <int>     <dbl>    <int>
## 1   2013     1     1      517            515         2      830
## 2   2013     1     1      533            529         4      850
## 3   2013     1     1      542            540         2      923
## 4   2013     1     1      544            545        -1     1004
## 5   2013     1     1      554            600        -6      812
## 6   2013     1     1      554            558        -4      740
## 7   2013     1     1      555            600        -5      913
## 8   2013     1     1      557            600        -3      709
## 9   2013     1     1      557            600        -3      838
## 10  2013     1     1      558            600        -2      753
## # ... with 832 more rows, and 12 more variables: sched_arr_time <int>,
## #   arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
## #   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## #   minute <dbl>, time_hour <dttm>

?base::Logic

store all x-mas flights

note, if you wrap the expression in () then the result will be displayed even when the result is assigned to a variable.

The

(xmas_flights <- filter(flights, month == 12, day == 24))

## # A tibble: 761 × 19
##     year month   day dep_time sched_dep_time dep_delay arr_time
##    <int> <int> <int>    <int>          <int>     <dbl>    <int>
## 1   2013    12    24        9           2359        10      444
## 2   2013    12    24      458            500        -2      652
## 3   2013    12    24      513            515        -2      813
## 4   2013    12    24      543            540         3      844
## 5   2013    12    24      546            550        -4     1032
## 6   2013    12    24      555            600        -5      851
## 7   2013    12    24      556            600        -4      845
## 8   2013    12    24      557            600        -3      908
## 9   2013    12    24      558            600        -2      827
## 10  2013    12    24      558            600        -2      729
## # ... with 751 more rows, and 12 more variables: sched_arr_time <int>,
## #   arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
## #   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## #   minute <dbl>, time_hour <dttm>

boolean operators work as well

filter(flights, month == 11 | month == 12)

## # A tibble: 55,403 × 19
##     year month   day dep_time sched_dep_time dep_delay arr_time
##    <int> <int> <int>    <int>          <int>     <dbl>    <int>
## 1   2013    11     1        5           2359         6      352
## 2   2013    11     1       35           2250       105      123
## 3   2013    11     1      455            500        -5      641
## 4   2013    11     1      539            545        -6      856
## 5   2013    11     1      542            545        -3      831
## 6   2013    11     1      549            600       -11      912
## 7   2013    11     1      550            600       -10      705
## 8   2013    11     1      554            600        -6      659
## 9   2013    11     1      554            600        -6      826
## 10  2013    11     1      554            600        -6      749
## # ... with 55,393 more rows, and 12 more variables: sched_arr_time <int>,
## #   arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
## #   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## #   minute <dbl>, time_hour <dttm>

the following expressions give the same result

filter(flights, !(arr_delay > 120 | dep_delay > 120))

## # A tibble: 316,050 × 19
##     year month   day dep_time sched_dep_time dep_delay arr_time
##    <int> <int> <int>    <int>          <int>     <dbl>    <int>
## 1   2013     1     1      517            515         2      830
## 2   2013     1     1      533            529         4      850
## 3   2013     1     1      542            540         2      923
## 4   2013     1     1      544            545        -1     1004
## 5   2013     1     1      554            600        -6      812
## 6   2013     1     1      554            558        -4      740
## 7   2013     1     1      555            600        -5      913
## 8   2013     1     1      557            600        -3      709
## 9   2013     1     1      557            600        -3      838
## 10  2013     1     1      558            600        -2      753
## # ... with 316,040 more rows, and 12 more variables: sched_arr_time <int>,
## #   arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
## #   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## #   minute <dbl>, time_hour <dttm>

filter(flights, arr_delay <= 120, dep_delay <= 120)

## # A tibble: 316,050 × 19
##     year month   day dep_time sched_dep_time dep_delay arr_time
##    <int> <int> <int>    <int>          <int>     <dbl>    <int>
## 1   2013     1     1      517            515         2      830
## 2   2013     1     1      533            529         4      850
## 3   2013     1     1      542            540         2      923
## 4   2013     1     1      544            545        -1     1004
## 5   2013     1     1      554            600        -6      812
## 6   2013     1     1      554            558        -4      740
## 7   2013     1     1      555            600        -5      913
## 8   2013     1     1      557            600        -3      709
## 9   2013     1     1      557            600        -3      838
## 10  2013     1     1      558            600        -2      753
## # ... with 316,040 more rows, and 12 more variables: sched_arr_time <int>,
## #   arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
## #   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## #   minute <dbl>, time_hour <dttm>

Arrange rows with arrange()

arrange(flights, year, month, day)

## # A tibble: 336,776 × 19
##     year month   day dep_time sched_dep_time dep_delay arr_time
##    <int> <int> <int>    <int>          <int>     <dbl>    <int>
## 1   2013     1     1      517            515         2      830
## 2   2013     1     1      533            529         4      850
## 3   2013     1     1      542            540         2      923
## 4   2013     1     1      544            545        -1     1004
## 5   2013     1     1      554            600        -6      812
## 6   2013     1     1      554            558        -4      740
## 7   2013     1     1      555            600        -5      913
## 8   2013     1     1      557            600        -3      709
## 9   2013     1     1      557            600        -3      838
## 10  2013     1     1      558            600        -2      753
## # ... with 336,766 more rows, and 12 more variables: sched_arr_time <int>,
## #   arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
## #   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## #   minute <dbl>, time_hour <dttm>

select columns with select()

also an easy way to bring columns in a specific order

select(flights, year, month, day)

## # A tibble: 336,776 × 3
##     year month   day
##    <int> <int> <int>
## 1   2013     1     1
## 2   2013     1     1
## 3   2013     1     1
## 4   2013     1     1
## 5   2013     1     1
## 6   2013     1     1
## 7   2013     1     1
## 8   2013     1     1
## 9   2013     1     1
## 10  2013     1     1
## # ... with 336,766 more rows

select all but a range of columns

select(flights, -(year:day))

## # A tibble: 336,776 × 16
##    dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay
##       <int>          <int>     <dbl>    <int>          <int>     <dbl>
## 1       517            515         2      830            819        11
## 2       533            529         4      850            830        20
## 3       542            540         2      923            850        33
## 4       544            545        -1     1004           1022       -18
## 5       554            600        -6      812            837       -25
## 6       554            558        -4      740            728        12
## 7       555            600        -5      913            854        19
## 8       557            600        -3      709            723       -14
## 9       557            600        -3      838            846        -8
## 10      558            600        -2      753            745         8
## # ... with 336,766 more rows, and 10 more variables: carrier <chr>,
## #   flight <int>, tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>,
## #   distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

more can be found in the cheatsheet

Add new variables with mutate()

note the %>% operator

select(flights, 
  year:day, 
  ends_with("delay"), 
  distance, 
  air_time) %>% 
mutate(
  gain = arr_delay - dep_delay,
  speed = distance / air_time * 60,
  hours = air_time / 60,
  gain_per_hour = gain / hours) %>% 
  select(-c(month, day, speed))

## # A tibble: 336,776 × 8
##     year dep_delay arr_delay distance air_time  gain     hours
##    <int>     <dbl>     <dbl>    <dbl>    <dbl> <dbl>     <dbl>
## 1   2013         2        11     1400      227     9 3.7833333
## 2   2013         4        20     1416      227    16 3.7833333
## 3   2013         2        33     1089      160    31 2.6666667
## 4   2013        -1       -18     1576      183   -17 3.0500000
## 5   2013        -6       -25      762      116   -19 1.9333333
## 6   2013        -4        12      719      150    16 2.5000000
## 7   2013        -5        19     1065      158    24 2.6333333
## 8   2013        -3       -14      229       53   -11 0.8833333
## 9   2013        -3        -8      944      140    -5 2.3333333
## 10  2013        -2         8      733      138    10 2.3000000
## # ... with 336,766 more rows, and 1 more variables: gain_per_hour <dbl>

if you only want to keep the new columns use “transmute()”

select(flights, 
  year:day, 
  ends_with("delay"), 
  distance, 
  air_time) %>% 
transmute(
  gain = arr_delay - dep_delay,
  speed = distance / air_time * 60,
  hours = air_time / 60,
  gain_per_hour = gain / hours)

## # A tibble: 336,776 × 4
##     gain    speed     hours gain_per_hour
##    <dbl>    <dbl>     <dbl>         <dbl>
## 1      9 370.0441 3.7833333      2.378855
## 2     16 374.2731 3.7833333      4.229075
## 3     31 408.3750 2.6666667     11.625000
## 4    -17 516.7213 3.0500000     -5.573770
## 5    -19 394.1379 1.9333333     -9.827586
## 6     16 287.6000 2.5000000      6.400000
## 7     24 404.4304 2.6333333      9.113924
## 8    -11 259.2453 0.8833333    -12.452830
## 9     -5 404.5714 2.3333333     -2.142857
## 10    10 318.6957 2.3000000      4.347826
## # ... with 336,766 more rows

Grouped summaries with summarise()

the mean of all depature delays

summarise(flights, delay = mean(dep_delay, na.rm = TRUE))

## # A tibble: 1 × 1
##      delay
##      <dbl>
## 1 12.63907

# na.rm a logical value indicating whether NA values should be stripped before the computation proceeds.

summarise(flights, delay = mean(dep_delay, na.rm = TRUE)) %>% as.numeric()

## [1] 12.63907

by_day <- group_by(flights, year, month, day)
summarise(by_day, delay = mean(dep_delay, na.rm = TRUE))

## Source: local data frame [365 x 4]
## Groups: year, month [?]
## 
##     year month   day     delay
##    <int> <int> <int>     <dbl>
## 1   2013     1     1 11.548926
## 2   2013     1     2 13.858824
## 3   2013     1     3 10.987832
## 4   2013     1     4  8.951595
## 5   2013     1     5  5.732218
## 6   2013     1     6  7.148014
## 7   2013     1     7  5.417204
## 8   2013     1     8  2.553073
## 9   2013     1     9  2.276477
## 10  2013     1    10  2.844995
## # ... with 355 more rows

find pattern of delays during the year

by_day <- flights %>% group_by(year, month)
summarise(by_day, delay = mean(dep_delay, na.rm = TRUE)) %>% ggplot(aes( x = month, y = delay, group = month)) +
  geom_col()

Find planes with high delays

not_cancelled <- flights %>% 
  filter(!is.na(arr_delay))

not_cancelled %>% 
  group_by(tailnum) %>% 
  summarise(
    delay = mean(arr_delay)
  ) %>%
ggplot( mapping = aes(x = delay)) + 
  geom_freqpoly(binwidth = 10)

there seems a few planes with very high mean delay. Lets look closer into the issue

delays <- not_cancelled %>% 
  group_by(tailnum) %>% 
  summarise(
    delay = mean(arr_delay, na.rm = TRUE),
    n = n()
  )

ggplot(data = delays, mapping = aes(x = n, y = delay)) + 
  geom_point(alpha = 1/10)

the high delays are for tailnum wiht limited number of flight. Lets choose only tailnums where at least 25 flights are recorded

delays %>% 
  filter(n > 25) %>% 
  ggplot(mapping = aes(x = n, y = delay)) + 
    geom_point(alpha = 1/10)

what if we want to select the points under consideration not via a limit but from a plot? Use Shiny Gadgets

library(shiny)
library(miniUI)

ggbrush <- function(data, xvar, yvar) {
  
  ui <- miniPage(
    gadgetTitleBar("Drag to select points"),
    miniContentPanel(
      # The brush="brush" argument means we can listen for
      # brush events on the plot using input$brush.
      plotOutput("plot", height = "100%", brush = "brush")
    )
  )
  
  server <- function(input, output, session) {
    
    # Render the plot
    output$plot <- renderPlot({
      # Plot the data with x/y vars indicated by the caller.
      ggplot(data, aes_string(xvar, yvar)) + geom_point()
    })
    
    # Handle the Done button being pressed.
    observeEvent(input$done, {
      # Return the brushed points. See ?shiny::brushedPoints.
      stopApp(brushedPoints(data, input$brush, allRows = TRUE))
    })
  }
  
  runGadget(ui, server)
}
# pick_points(mtcars, ~wt, ~mpg)
brushed_points <- ggbrush(delays, "n", "delay")

## 
## Listening on http://127.0.0.1:7592

brushed_points   %>% ggplot(mapping = aes(x = n, y = delay, color = selected_)) + 
    geom_point(alpha = 1/10)

brushed_points   %>% filter(selected_ ==TRUE)  %>%  ggplot(mapping = aes(x = n, y = delay, color = selected_)) + 
    geom_point(alpha = 1/3)

Now get your hands dirty on select, summarise, mutate…

now wrangle you data to analyse it use

summerise
mutate
select
group_by
filter

Now a few more things we need for the EuropeLeagueTransfers.Rmd

left_join

the data set nycflights13 has four tibbles (dataframes)

airlines
airports
planes
weather

 airlines

## # A tibble: 16 × 2
##    carrier                        name
##      <chr>                       <chr>
## 1       9E           Endeavor Air Inc.
## 2       AA      American Airlines Inc.
## 3       AS        Alaska Airlines Inc.
## 4       B6             JetBlue Airways
## 5       DL        Delta Air Lines Inc.
## 6       EV    ExpressJet Airlines Inc.
## 7       F9      Frontier Airlines Inc.
## 8       FL AirTran Airways Corporation
## 9       HA      Hawaiian Airlines Inc.
## 10      MQ                   Envoy Air
## 11      OO       SkyWest Airlines Inc.
## 12      UA       United Air Lines Inc.
## 13      US             US Airways Inc.
## 14      VX              Virgin America
## 15      WN      Southwest Airlines Co.
## 16      YV          Mesa Airlines Inc.

 airports

## # A tibble: 1,458 × 8
##      faa                           name      lat        lon   alt    tz
##    <chr>                          <chr>    <dbl>      <dbl> <int> <dbl>
## 1    04G              Lansdowne Airport 41.13047  -80.61958  1044    -5
## 2    06A  Moton Field Municipal Airport 32.46057  -85.68003   264    -6
## 3    06C            Schaumburg Regional 41.98934  -88.10124   801    -6
## 4    06N                Randall Airport 41.43191  -74.39156   523    -5
## 5    09J          Jekyll Island Airport 31.07447  -81.42778    11    -5
## 6    0A9 Elizabethton Municipal Airport 36.37122  -82.17342  1593    -5
## 7    0G6        Williams County Airport 41.46731  -84.50678   730    -5
## 8    0G7  Finger Lakes Regional Airport 42.88356  -76.78123   492    -5
## 9    0P2   Shoestring Aviation Airfield 39.79482  -76.64719  1000    -5
## 10   0S9          Jefferson County Intl 48.05381 -122.81064   108    -8
## # ... with 1,448 more rows, and 2 more variables: dst <chr>, tzone <chr>

 planes

## # A tibble: 3,322 × 9
##    tailnum  year                    type     manufacturer     model
##      <chr> <int>                   <chr>            <chr>     <chr>
## 1   N10156  2004 Fixed wing multi engine          EMBRAER EMB-145XR
## 2   N102UW  1998 Fixed wing multi engine AIRBUS INDUSTRIE  A320-214
## 3   N103US  1999 Fixed wing multi engine AIRBUS INDUSTRIE  A320-214
## 4   N104UW  1999 Fixed wing multi engine AIRBUS INDUSTRIE  A320-214
## 5   N10575  2002 Fixed wing multi engine          EMBRAER EMB-145LR
## 6   N105UW  1999 Fixed wing multi engine AIRBUS INDUSTRIE  A320-214
## 7   N107US  1999 Fixed wing multi engine AIRBUS INDUSTRIE  A320-214
## 8   N108UW  1999 Fixed wing multi engine AIRBUS INDUSTRIE  A320-214
## 9   N109UW  1999 Fixed wing multi engine AIRBUS INDUSTRIE  A320-214
## 10  N110UW  1999 Fixed wing multi engine AIRBUS INDUSTRIE  A320-214
## # ... with 3,312 more rows, and 4 more variables: engines <int>,
## #   seats <int>, speed <int>, engine <chr>

 weather

## # A tibble: 26,130 × 15
##    origin  year month   day  hour  temp  dewp humid wind_dir wind_speed
##     <chr> <dbl> <dbl> <int> <int> <dbl> <dbl> <dbl>    <dbl>      <dbl>
## 1     EWR  2013     1     1     0 37.04 21.92 53.97      230   10.35702
## 2     EWR  2013     1     1     1 37.04 21.92 53.97      230   13.80936
## 3     EWR  2013     1     1     2 37.94 21.92 52.09      230   12.65858
## 4     EWR  2013     1     1     3 37.94 23.00 54.51      230   13.80936
## 5     EWR  2013     1     1     4 37.94 24.08 57.04      240   14.96014
## 6     EWR  2013     1     1     6 39.02 26.06 59.37      270   10.35702
## 7     EWR  2013     1     1     7 39.02 26.96 61.63      250    8.05546
## 8     EWR  2013     1     1     8 39.02 28.04 64.43      240   11.50780
## 9     EWR  2013     1     1     9 39.92 28.04 62.21      250   12.65858
## 10    EWR  2013     1     1    10 39.02 28.04 64.43      260   12.65858
## # ... with 26,120 more rows, and 5 more variables: wind_gust <dbl>,
## #   precip <dbl>, pressure <dbl>, visib <dbl>, time_hour <dttm>

find the links between the data.frames

library(visNetwork)
# this function creates a data.frame with the name of the data.frame and the names of the columns of that data.frame
create_df_of_names = function(df, name){
  data.frame(from = name, to = names(df))
}

# create a names list of the data.frames
list_of_df <- list(flights = flights,airlines = airlines, airports = airports, weather = weather, planes = planes) 
# and map them to build one data.frame with two columns
# - from contains all  data.frame names
# - to  contains all column names
edge <- map2_df(list_of_df,names(list_of_df), create_df_of_names)

## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character

## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character

# create a visNetwork

nodesFrom <-  edge %>% cbind(unlist(.$from),"Table") %>% select(3,4) %>% data.frame  
nodesTo <-  edge %>% cbind(unlist(.$to),"Attribute") %>% select(3,4) %>% data.frame 

names(nodesFrom) <- c("id", "group")
names(nodesTo) <- c("id", "group")

nodes <- rbind(nodesFrom,nodesTo) %>% unique() 
nodes$id <- as.character((nodes$id))  
nodes <- nodes %>% unique() %>% arrange(id)
visNetwork(nodes, edge)%>%
  visOptions(highlightNearest = list(enabled = TRUE, degree = 2), nodesIdSelection = TRUE) %>%
  visEdges(arrows = "to") %>%  
  visGroups(groupname = "Table",     shape = "icon", icon = list(code = "f114", color = "green",size = 75)) %>%
  visGroups(groupname = "Attribute", shape = "icon", icon = list(code = "f115", color = "lightgreen", size = 45)) %>%
  addFontAwesome()

# list of icons http://astronautweb.co/snippet/font-awesome/

lets find out which manufacturer has the highest delays

first we need to join flights with planes

flight_planes <- left_join(flights, planes, by = "tailnum")

flight_planes %>% group_by(manufacturer) %>% summarise(delay_per_flight = sum(arr_delay, na.rm = TRUE)/ n(),number_of_flights = n()) %>% arrange(desc(delay_per_flight))

## # A tibble: 36 × 3
##              manufacturer delay_per_flight number_of_flights
##                     <chr>            <dbl>             <int>
## 1              AGUSTA SPA        29.687500                32
## 2                CANADAIR        15.601004              1594
## 3                 EMBRAER        13.384952             66068
## 4      AVIAT AIRCRAFT INC        11.611111                18
## 5          FRIEDEMANN JON        11.507937                63
## 6           BARKER JACK L        11.027778               252
## 7         LAMBERT RICHARD         8.907407                54
## 8                  CESSNA         8.164134               658
## 9      CIRRUS DESIGN CORP         8.109966               291
## 10 ROBINSON HELICOPTER CO         8.041958               286
## # ... with 26 more rows

lets find out which airline has the highest delays

first we need to join flights with planes

flight_airlines <- left_join(flights, airlines)

## Joining, by = "carrier"

flight_airlines %>% group_by(name) %>% summarise(delay_per_flight = sum(arr_delay, na.rm = TRUE)/ n(),number_of_flights = n()) %>% arrange(desc(delay_per_flight))

## # A tibble: 16 × 3
##                           name delay_per_flight number_of_flights
##                          <chr>            <dbl>             <int>
## 1       Frontier Airlines Inc.       21.7927007               685
## 2  AirTran Airways Corporation       19.5914110              3260
## 3     ExpressJet Airlines Inc.       14.9027006             54173
## 4           Mesa Airlines Inc.       14.0815308               601
## 5        SkyWest Airlines Inc.       10.8125000                32
## 6                    Envoy Air       10.2196083             26397
## 7       Southwest Airlines Co.        9.4675356             12275
## 8              JetBlue Airways        9.3565297             54635
## 9            Endeavor Air Inc.        6.9135428             18460
## 10       United Air Lines Inc.        3.5044575             58665
## 11             US Airways Inc.        2.0564862             20536
## 12              Virgin America        1.7487408              5162
## 13        Delta Air Lines Inc.        1.6288921             48110
## 14      American Airlines Inc.        0.3555868             32729
## 15      Hawaiian Airlines Inc.       -6.9152047               342
## 16        Alaska Airlines Inc.       -9.8613445               714

long and wide data.frames

for some operations the tidy wide format is not suitable as input to an operation, then a “long” version of the data.frame can be generated using the “melt” command.

A further example will be shown in EuropeLeagueTransfers.Rmd and further information on the topic can be found at http://seananderson.ca/2013/10/19/reshape.html

library(reshape2)

## 
## Attaching package: 'reshape2'

## The following object is masked from 'package:tidyr':
## 
##     smiths

names(airquality) <- tolower(names(airquality))
aqm <- melt(airquality, id=c("month", "day"),
  variable.name = "climate_variable", 
  value.name = "climate_value")
head(airquality)

##   ozone solar.r wind temp month day
## 1    41     190  7.4   67     5   1
## 2    36     118  8.0   72     5   2
## 3    12     149 12.6   74     5   3
## 4    18     313 11.5   62     5   4
## 5    NA      NA 14.3   56     5   5
## 6    28      NA 14.9   66     5   6

head(aqm)

##   month day climate_variable climate_value
## 1     5   1            ozone            41
## 2     5   2            ozone            36
## 3     5   3            ozone            12
## 4     5   4            ozone            18
## 5     5   5            ozone            NA
## 6     5   6            ozone            28

tail(aqm)

##     month day climate_variable climate_value
## 607     9  25             temp            63
## 608     9  26             temp            70
## 609     9  27             temp            77
## 610     9  28             temp            75
## 611     9  29             temp            76
## 612     9  30             temp            68

Cast functions cast (deutsch: gießen) a molten data frame into an array or data frame. is the reverse function of melt and will be used in EuropeLeagueTransfers.Rmd

aqw <- dcast(aqm, month + day ~ climate_variable)

## Using climate_value as value column: use value.var to override.

head(aqw)

##   month day ozone solar.r wind temp
## 1     5   1    41     190  7.4   67
## 2     5   2    36     118  8.0   72
## 3     5   3    12     149 12.6   74
## 4     5   4    18     313 11.5   62
## 5     5   5    NA      NA 14.3   56
## 6     5   6    28      NA 14.9   66

last thing we need for EuropeLeagueTransfers.Rmd

grepl returns a logic vector given an expression

letters

##  [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q"
## [18] "r" "s" "t" "u" "v" "w" "x" "y" "z"

grep("[a-c]", letters)

## [1] 1 2 3

grep("[a-z]", letters)

##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
## [24] 24 25 26

grepl("[a-c]", letters)

##  [1]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSE FALSE FALSE FALSE

grepl("[a-z]", letters)

##  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [15] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

Lets dive into some code

EuropeLeagueTransfers.Rmd

R Kenntnisse VHS 1_2017

Data visualisation

Where can I find useful packages?

Where can I find how to use packages

Analyse data

Creating a ggplot

Create a ggplot with color = class

Create a ggplot with size = cty

Create a ggplot with alpha = class

Create a ggplot with shape = class

Create plot where property of geom is set manually

Recap

Facets

facet_wrap

facet_grid

Now get your hands dirty on size, color, shape, alpha

Geometic objects

geom_smooth with more than one line

avoid the legend

display several geoms in same plot

don’t repeat code

use only subset of data for geom

bar plot for discrete x-data

lost in all the options?

Statistical transformations

box plot for discrete x- and continuous y-data

Violin plot for discrete x- and continuous y-data

Histogram

use geom_freqpoly for easier comparison

Empirical Cumulative Distribution Function (ECDF)

Find correlations

Maximal Information Coefficient (MIC)

Recap

Now get your hands dirty on: facets, geom_ …

Data wrangling

filter rows

store all x-mas flights

boolean operators work as well

Arrange rows with arrange()

select columns with select()

Add new variables with mutate()

Grouped summaries with summarise()

Find planes with high delays

Now get your hands dirty on select, summarise, mutate…

Now a few more things we need for the EuropeLeagueTransfers.Rmd

left_join

find the links between the data.frames

lets find out which manufacturer has the highest delays

lets find out which airline has the highest delays

long and wide data.frames

last thing we need for EuropeLeagueTransfers.Rmd

Lets dive into some code